Enhancing Spatiotemporal Resampling with a Novel MIS Weight
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
In real-time rendering, optimizing the sampling of large-scale candidates is crucial. The spatiotemporal reservoir resampling (ReSTIR) method provides an effective approach for handling large candidate samples, while the Generalized Resampled Importance Sampling (GRIS) theory provides a general framework for resampling algorithms. However, we have observed that when using the generalized multiple importance sampling (MIS) weight in previous work during spatiotemporal reuse, variances gradually amplify in the candidate domain when there are significant differences. To address this issue, we propose a new MIS weight suitable for resampling that blends samples from different sampling domains, ensuring convergence of results as the proportion of non-canonical samples increases. Additionally, we apply this weight to temporal resampling to reduce noise caused by scene changes or jitter. Our method effectively reduces energy loss in the biased version of ReSTIR DI while incurring no additional overhead, and it also suppresses artifacts caused by a high proportion of temporal samples. As a result, our approach leads to lower variance in the sampling results.
Description
CCS Concepts: Computing methodologies -> Rendering; Ray tracing; Monte Carlo algorithms
@article{10.1111:cgf.15049,
journal = {Computer Graphics Forum},
title = {{Enhancing Spatiotemporal Resampling with a Novel MIS Weight}},
author = {Pan, Xingyue and Zhang, Jiaxuan and Huang, Jiancong and Liu, Ligang},
year = {2024},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.15049}
}